Data Science Basics

Code:

CC4088

Acronym:

CC4088

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Nanomaterials Science and Technology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:CTN	5	Official Study Plan since 2020_M:CTN	2	-	3	21	81
M:Q	1	Study plan since academic year 2023/2024	2	-	3	21	81

Teaching language

English

Objectives

Students will obtain a global perspective on the different steps of a Data Science project, focusing on classification and regression. For each of these steps, some of the main techniques and methods will be presented while further details will be addressed in more specific courses.

Learning outcomes and competences

Students should:
- know all the steps of a data science project and its most common operations;
- identify different types of data science problems;
- justifiably select appropriate methods, algorithms and tools to solve these problems
- justifiably apply methods, algorithms and tools to solve these problems
- evaluate the results

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Programming knowledge, especially in Python or R Knowledge of statistics

Program

Mandatory literature

Jake VanderPlas; Python Data Science Handbook, O'Reilly, 2016. ISBN: 978-1-491-91205-8
Jiawei Han, Micheline Kamber and Jian Pei; Data Mining Concepts and Technique, Morgan Kaufmann, 2012

Teaching methods and learning activities

Tutorial classes with theory exposition and problem solving activities.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	65,00
Teste	35,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	50,00
Estudo autónomo	10,00
Frequência das aulas	21,00
Total:	81,00

Eligibility for exams

Grade above zero in the assignment and in the test. Answer to class quastions submitted online.

Calculation formula of final grade

There will be one test and one group assignment.

There will be activities to promote participation and feedback such as class questions and group discussions.

The final grade is given by the weighted average of theoretical and practical grades according to the following formula:

Final Grade.0 = 0.65 x GradeAssignment + 0.35 x GradeTest

FinalGrade = min (FinalGrade.0, GradeTest*1.4)

Classification improvement

Assignments are not subject to improvement in the appeal season